# Manhattan Room Layout Reconstruction from a Single 360 image: A Comparative Study of State-of-the-art Methods

arXiv: https://arxiv.org/abs/1910.04099 · HTML: https://mlsanity.com/paper/1910.04099/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2019.

## Built on by (most cited first, 11 of 11)

- [LED2-Net: Monocular 360 Layout Estimation via Differentiable Depth Rendering](https://mlsanity.com/paper/2104.00568/) (2021)
- [MonteFloor: Extending MCTS for Reconstructing Accurate Large-Scale Floor Plans](https://mlsanity.com/paper/2103.11161/) (2021)
- [Monte Carlo Scene Search for 3D Scene Understanding](https://mlsanity.com/paper/2103.07969/) (2021)
- [PanoDR: Spherical Panorama Diminished Reality for Indoor Scenes](https://mlsanity.com/paper/2106.00446/) (2021)
- [SSLayout360: Semi-Supervised Indoor Layout Estimation from 360-Degree Panorama](https://mlsanity.com/paper/2103.13696/) (2021)
- [Single-Shot Cuboids: Geodesics-based End-to-end Manhattan Aligned Layout Estimation from Spherical Panoramas](https://mlsanity.com/paper/2102.03939/) (2021)
- [HiMODE: A Hybrid Monocular Omnidirectional Depth Estimation Model](https://mlsanity.com/paper/2204.05007/) (2022)
- [360-MLC: Multi-view Layout Consistency for Self-training and Hyper-parameter Tuning](https://mlsanity.com/paper/2210.12935/) (2022)
- [Monocular Spherical Depth Estimation with Explicitly Connected Weak Layout Cues](https://mlsanity.com/paper/2206.11358/) (2022)
- [PanoViT: Vision Transformer for Room Layout Estimation from a Single Panoramic Image](https://mlsanity.com/paper/2212.12156/) (2022)
- [Semi-Supervised 360 Layout Estimation with Panoramic Collaborative Perturbations](https://mlsanity.com/paper/2503.01114/) (2025)

## Abstract (arXiv)

Recent approaches for predicting layouts from 360 panoramas produce excellent results. These approaches build on a common framework consisting of three steps: a pre-processing step based on edge-based alignment, prediction of layout elements, and a post-processing step by fitting a 3D layout to the layout elements. Until now, it has been difficult to compare the methods due to multiple different design decisions, such as the encoding network (e.g. SegNet or ResNet), type of elements predicted (e.g. corners, wall/floor boundaries, or semantic segmentation), or method of fitting the 3D layout. To address this challenge, we summarize and describe the common framework, the variants, and the impact of the design decisions. For a complete evaluation, we also propose extended annotations for the Matterport3D dataset [3], and introduce two depth-based evaluation metrics.